Position Overview

Join us in a high-impact, high-visibility role where you will lead the design and delivery of enterprise-scale Data & AI solutions that power analytics, operational decision-making, and AI innovation across the organization.

We are seeking an experienced Senior Lead Data & AI Engineer with 9-12+ years of experience in Data Engineering and contemporary cloud-based data platforms, with deep expertise in Databricks, Lakehouse architecture, and AWS. This role will be responsible for building scalable data foundations, reusable engineering frameworks, and AI-ready data products that accelerate business value and enable advanced analytics and Generative AI use cases.

The ideal candidate combines strong hands-on data engineering expertise with solution architecture, technical leadership, and a passion for modernizing data ecosystems through automation, metadata-driven engineering, and cloud-native technologies.

Key Responsibilities

Data Engineering Leadership

- Lead the architecture, design, and delivery of enterprise-scale data engineering solutions leveraging Databricks and AWS.
- Design and implement scalable, resilient, and reusable data pipelines supporting batch, streaming, and event-driven workloads.
- Drive engineering best practices across data ingestion, transformation, orchestration, testing, monitoring, and deployment.
- Establish reusable frameworks, patterns, and standards to accelerate delivery and improve engineering quality across teams.

Databricks & Lakehouse Engineering

- Architect and implement modern Lakehouse solutions utilizing Databricks, Delta Lake, Unity Catalog, and cloud-native services.




- Develop metadata-driven and configuration-based frameworks that standardize ingestion, transformation, quality validation, and deployment processes.
- Design and implement Delta Live Tables (DLT), Lakeflow Pipelines, and Databricks Workflows to automate enterprise data processing.
- Build trusted, governed, and reusable data products that support analytics, reporting, data science, and AI initiatives.
- Optimize platform performance, scalability, reliability, and cost efficiency across large-scale workloads.

Data Platform Modernization

- Lead migration and modernization initiatives from legacy platforms to cloud-native data architectures.
- Implement data observability, monitoring, lineage, metadata management, and quality controls throughout the data lifecycle.
- Partner with architecture and platform teams to advance enterprise data platform capabilities and engineering standards.
- Support implementation of CI/CD, Infrastructure-as-Code, automated testing, and DevSecOps practices.

AI & Advanced Analytics Enablement

- Build AI-ready data foundations that support machine learning, Generative AI, and intelligent business applications.
- Partner with Data Scientists, AI Engineers, and business teams to operationalize data products and AI solutions.
- Support implementation of vector search, semantic models, retrieval-augmented generation (RAG),



and knowledge-driven AI capabilities.
- Contribute to AI orchestration and agent-based solutions where business value can be accelerated through intelligent automation.
- Enable reliable integration between AI applications and enterprise data platforms.

Governance, Security & Data Quality

- Implement enterprise-grade governance using Unity Catalog, metadata repositories, and data quality frameworks.
- Ensure compliance with enterprise security, privacy, and regulatory requirements.
- Design automated controls for data validation, observability, lineage, and operational monitoring.
- Partner with governance organizations to improve trust and accessibility of critical data assets.

Stakeholder & Team Leadership

- Act as a trusted technical advisor to business, analytics, and technology stakeholders.
- Mentor and provide technical leadership to Data Engineers and platform teams.
- Lead architecture reviews, design workshops, and engineering governance activities.
- Translate complex technical concepts into actionable business outcomes for leadership audiences.

Qualifications

- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or related discipline.
- 9-12+ years of experience in Data Engineering, Data Platform Engineering, or Analytics Engineering.
- 5+ years of hands-on experience building enterprise solutions on Databricks.
- Strong expertise in Lakehouse architecture, Delta Lake, Unity Catalog, and modern data engineering patterns.
- Proven experience building large-scale data pipelines using Spark, PySpark, SQL, and Python.
- Deep

📌 Senior Lead Data (Toronto)
🏢 Vanguard
📍 Toronto